Overview

Dataset statistics

Number of variables17
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory140.3 B

Variable types

Numeric17

Alerts

W is highly overall correlated with SO and 2 other fieldsHigh correlation
R is highly overall correlated with HR and 1 other fieldsHigh correlation
AB is highly overall correlated with HR and 2 other fieldsHigh correlation
H is highly overall correlated with SB and 2 other fieldsHigh correlation
2B is highly overall correlated with BBHigh correlation
HR is highly overall correlated with R and 4 other fieldsHigh correlation
RA is highly overall correlated with HR and 4 other fieldsHigh correlation
ER is highly overall correlated with H and 3 other fieldsHigh correlation
ERA is highly overall correlated with W and 5 other fieldsHigh correlation
SHO is highly overall correlated with 3B and 7 other fieldsHigh correlation
SV is highly overall correlated with W and 7 other fieldsHigh correlation
3B is highly overall correlated with CG and 1 other fieldsHigh correlation
BB is highly overall correlated with AB and 2 other fieldsHigh correlation
SO is highly overall correlated with W and 3 other fieldsHigh correlation
SB is highly overall correlated with H and 4 other fieldsHigh correlation
CG is highly overall correlated with 3B and 2 other fieldsHigh correlation
E is highly overall correlated with SO and 2 other fieldsHigh correlation
RA has unique valuesUnique
ER has unique valuesUnique
ERA has unique valuesUnique
CG has 3 (10.0%) zerosZeros

Reproduction

Analysis started2022-11-24 08:10:12.404318
Analysis finished2022-11-24 08:11:36.067395
Duration1 minute and 23.66 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

W
Real number (ℝ)

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.966667
Minimum63
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:36.285389image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum63
5-th percentile65.35
Q174
median81
Q387.75
95-th percentile97.55
Maximum100
Range37
Interquartile range (IQR)13.75

Descriptive statistics

Standard deviation10.453455
Coefficient of variation (CV)0.12910813
Kurtosis-0.87426245
Mean80.966667
Median Absolute Deviation (MAD)7
Skewness0.047088689
Sum2429
Variance109.27471
MonotonicityNot monotonic
2022-11-24T13:41:36.531169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
68 3
 
10.0%
81 2
 
6.7%
76 2
 
6.7%
74 2
 
6.7%
83 2
 
6.7%
98 1
 
3.3%
84 1
 
3.3%
92 1
 
3.3%
63 1
 
3.3%
67 1
 
3.3%
Other values (14) 14
46.7%
ValueCountFrequency (%)
63 1
 
3.3%
64 1
 
3.3%
67 1
 
3.3%
68 3
10.0%
71 1
 
3.3%
74 2
6.7%
76 2
6.7%
78 1
 
3.3%
79 1
 
3.3%
80 1
 
3.3%
ValueCountFrequency (%)
100 1
3.3%
98 1
3.3%
97 1
3.3%
95 1
3.3%
93 1
3.3%
92 1
3.3%
90 1
3.3%
88 1
3.3%
87 1
3.3%
86 1
3.3%

R
Real number (ℝ)

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean688.23333
Minimum573
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:36.805513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum573
5-th percentile617.05
Q1651.25
median689
Q3718.25
95-th percentile758.15
Maximum891
Range318
Interquartile range (IQR)67

Descriptive statistics

Standard deviation58.761754
Coefficient of variation (CV)0.085380569
Kurtosis3.8608701
Mean688.23333
Median Absolute Deviation (MAD)34.5
Skewness1.2007864
Sum20647
Variance3452.9437
MonotonicityNot monotonic
2022-11-24T13:41:37.116311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
689 2
 
6.7%
696 2
 
6.7%
724 1
 
3.3%
647 1
 
3.3%
650 1
 
3.3%
720 1
 
3.3%
667 1
 
3.3%
626 1
 
3.3%
573 1
 
3.3%
613 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
573 1
3.3%
613 1
3.3%
622 1
3.3%
626 1
3.3%
640 1
3.3%
644 1
3.3%
647 1
3.3%
650 1
3.3%
655 1
3.3%
656 1
3.3%
ValueCountFrequency (%)
891 1
3.3%
764 1
3.3%
751 1
3.3%
748 1
3.3%
737 1
3.3%
729 1
3.3%
724 1
3.3%
720 1
3.3%
713 1
3.3%
703 1
3.3%

AB
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5516.2667
Minimum5385
Maximum5649
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:37.369691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum5385
5-th percentile5418.35
Q15464
median5510
Q35570
95-th percentile5635.95
Maximum5649
Range264
Interquartile range (IQR)106

Descriptive statistics

Standard deviation70.467372
Coefficient of variation (CV)0.012774468
Kurtosis-0.77207884
Mean5516.2667
Median Absolute Deviation (MAD)54
Skewness0.18343656
Sum165488
Variance4965.6506
MonotonicityNot monotonic
2022-11-24T13:41:37.648089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5485 2
 
6.7%
5575 1
 
3.3%
5631 1
 
3.3%
5457 1
 
3.3%
5649 1
 
3.3%
5565 1
 
3.3%
5385 1
 
3.3%
5529 1
 
3.3%
5420 1
 
3.3%
5463 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
5385 1
3.3%
5417 1
3.3%
5420 1
3.3%
5428 1
3.3%
5439 1
3.3%
5457 1
3.3%
5459 1
3.3%
5463 1
3.3%
5467 1
3.3%
5480 1
3.3%
ValueCountFrequency (%)
5649 1
3.3%
5640 1
3.3%
5631 1
3.3%
5605 1
3.3%
5600 1
3.3%
5575 1
3.3%
5572 1
3.3%
5571 1
3.3%
5567 1
3.3%
5565 1
3.3%

H
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1403.5333
Minimum1324
Maximum1515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:37.910775image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1324
5-th percentile1335.5
Q11363
median1382.5
Q31451.5
95-th percentile1496.1
Maximum1515
Range191
Interquartile range (IQR)88.5

Descriptive statistics

Standard deviation57.140923
Coefficient of variation (CV)0.040712195
Kurtosis-0.89023632
Mean1403.5333
Median Absolute Deviation (MAD)32.5
Skewness0.67025372
Sum42106
Variance3265.0851
MonotonicityNot monotonic
2022-11-24T13:41:38.231186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1363 2
 
6.7%
1497 1
 
3.3%
1386 1
 
3.3%
1324 1
 
3.3%
1494 1
 
3.3%
1486 1
 
3.3%
1346 1
 
3.3%
1374 1
 
3.3%
1361 1
 
3.3%
1420 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
1324 1
3.3%
1331 1
3.3%
1341 1
3.3%
1346 1
3.3%
1349 1
3.3%
1351 1
3.3%
1361 1
3.3%
1363 2
6.7%
1370 1
3.3%
1374 1
3.3%
ValueCountFrequency (%)
1515 1
3.3%
1497 1
3.3%
1495 1
3.3%
1494 1
3.3%
1486 1
3.3%
1480 1
3.3%
1479 1
3.3%
1462 1
3.3%
1420 1
3.3%
1419 1
3.3%

2B
Real number (ℝ)

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean274.73333
Minimum236
Maximum308
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:38.492816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum236
5-th percentile244.35
Q1262.25
median275.5
Q3288.75
95-th percentile301.65
Maximum308
Range72
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation18.095405
Coefficient of variation (CV)0.065865342
Kurtosis-0.44018475
Mean274.73333
Median Absolute Deviation (MAD)13.5
Skewness-0.23064992
Sum8242
Variance327.44368
MonotonicityNot monotonic
2022-11-24T13:41:38.731581image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
272 3
 
10.0%
260 2
 
6.7%
289 2
 
6.7%
278 2
 
6.7%
277 2
 
6.7%
274 2
 
6.7%
288 2
 
6.7%
300 1
 
3.3%
292 1
 
3.3%
251 1
 
3.3%
Other values (12) 12
40.0%
ValueCountFrequency (%)
236 1
 
3.3%
243 1
 
3.3%
246 1
 
3.3%
251 1
 
3.3%
257 1
 
3.3%
260 2
6.7%
262 1
 
3.3%
263 1
 
3.3%
265 1
 
3.3%
272 3
10.0%
ValueCountFrequency (%)
308 1
3.3%
303 1
3.3%
300 1
3.3%
295 1
3.3%
294 1
3.3%
292 1
3.3%
289 2
6.7%
288 2
6.7%
279 1
3.3%
278 2
6.7%

3B
Real number (ℝ)

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.3
Minimum13
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:38.977009image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile17
Q123
median31
Q339
95-th percentile48.55
Maximum49
Range36
Interquartile range (IQR)16

Descriptive statistics

Standard deviation10.452355
Coefficient of variation (CV)0.33394105
Kurtosis-0.98496568
Mean31.3
Median Absolute Deviation (MAD)8.5
Skewness0.12950245
Sum939
Variance109.25172
MonotonicityNot monotonic
2022-11-24T13:41:39.229515image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
27 3
 
10.0%
39 2
 
6.7%
49 2
 
6.7%
17 2
 
6.7%
32 2
 
6.7%
26 2
 
6.7%
42 1
 
3.3%
48 1
 
3.3%
37 1
 
3.3%
18 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
13 1
 
3.3%
17 2
6.7%
18 1
 
3.3%
19 1
 
3.3%
20 1
 
3.3%
21 1
 
3.3%
22 1
 
3.3%
26 2
6.7%
27 3
10.0%
29 1
 
3.3%
ValueCountFrequency (%)
49 2
6.7%
48 1
3.3%
46 1
3.3%
44 1
3.3%
42 1
3.3%
40 1
3.3%
39 2
6.7%
37 1
3.3%
36 1
3.3%
34 1
3.3%

HR
Real number (ℝ)

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean163.63333
Minimum100
Maximum232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:39.545059image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile124.5
Q1140.25
median158.5
Q3177
95-th percentile224.15
Maximum232
Range132
Interquartile range (IQR)36.75

Descriptive statistics

Standard deviation31.823309
Coefficient of variation (CV)0.19447938
Kurtosis0.016806225
Mean163.63333
Median Absolute Deviation (MAD)18.5
Skewness0.51644074
Sum4909
Variance1012.723
MonotonicityNot monotonic
2022-11-24T13:41:39.859707image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
136 2
 
6.7%
167 2
 
6.7%
177 2
 
6.7%
139 1
 
3.3%
137 1
 
3.3%
148 1
 
3.3%
154 1
 
3.3%
187 1
 
3.3%
130 1
 
3.3%
100 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
100 1
3.3%
120 1
3.3%
130 1
3.3%
136 2
6.7%
137 1
3.3%
139 1
3.3%
140 1
3.3%
141 1
3.3%
145 1
3.3%
146 1
3.3%
ValueCountFrequency (%)
232 1
3.3%
230 1
3.3%
217 1
3.3%
212 1
3.3%
198 1
3.3%
187 1
3.3%
186 1
3.3%
177 2
6.7%
176 1
3.3%
172 1
3.3%

BB
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean469.1
Minimum375
Maximum570
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:40.183612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum375
5-th percentile384.8
Q1428.25
median473
Q3501.25
95-th percentile565.2
Maximum570
Range195
Interquartile range (IQR)73

Descriptive statistics

Standard deviation57.053725
Coefficient of variation (CV)0.1216238
Kurtosis-0.79827198
Mean469.1
Median Absolute Deviation (MAD)37.5
Skewness0.15849848
Sum14073
Variance3255.1276
MonotonicityNot monotonic
2022-11-24T13:41:40.487188image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
478 2
 
6.7%
383 1
 
3.3%
461 1
 
3.3%
426 1
 
3.3%
490 1
 
3.3%
457 1
 
3.3%
563 1
 
3.3%
387 1
 
3.3%
471 1
 
3.3%
375 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
375 1
3.3%
383 1
3.3%
387 1
3.3%
388 1
3.3%
404 1
3.3%
412 1
3.3%
418 1
3.3%
426 1
3.3%
435 1
3.3%
436 1
3.3%
ValueCountFrequency (%)
570 1
3.3%
567 1
3.3%
563 1
3.3%
554 1
3.3%
539 1
3.3%
533 1
3.3%
506 1
3.3%
503 1
3.3%
496 1
3.3%
490 1
3.3%

SO
Real number (ℝ)

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1248.2
Minimum973
Maximum1518
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:40.821547image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum973
5-th percentile1112.4
Q11157.5
median1261.5
Q31311.5
95-th percentile1370.4
Maximum1518
Range545
Interquartile range (IQR)154

Descriptive statistics

Standard deviation103.75947
Coefficient of variation (CV)0.083127279
Kurtosis1.4017054
Mean1248.2
Median Absolute Deviation (MAD)63
Skewness-0.15606548
Sum37446
Variance10766.028
MonotonicityNot monotonic
2022-11-24T13:41:41.137273image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1150 2
 
6.7%
973 1
 
3.3%
1267 1
 
3.3%
1327 1
 
3.3%
1312 1
 
3.3%
1159 1
 
3.3%
1258 1
 
3.3%
1274 1
 
3.3%
1107 1
 
3.3%
1344 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
973 1
3.3%
1107 1
3.3%
1119 1
3.3%
1148 1
3.3%
1150 2
6.7%
1151 1
3.3%
1157 1
3.3%
1159 1
3.3%
1227 1
3.3%
1231 1
3.3%
ValueCountFrequency (%)
1518 1
3.3%
1392 1
3.3%
1344 1
3.3%
1336 1
3.3%
1331 1
3.3%
1327 1
3.3%
1322 1
3.3%
1312 1
3.3%
1310 1
3.3%
1299 1
3.3%

SB
Real number (ℝ)

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean83.5
Minimum44
Maximum134
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:41.408754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum44
5-th percentile51.45
Q169
median83.5
Q396.5
95-th percentile127.05
Maximum134
Range90
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation22.815225
Coefficient of variation (CV)0.27323623
Kurtosis-0.071246866
Mean83.5
Median Absolute Deviation (MAD)14.5
Skewness0.47989287
Sum2505
Variance520.53448
MonotonicityNot monotonic
2022-11-24T13:41:41.689583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
69 3
 
10.0%
88 2
 
6.7%
78 1
 
3.3%
82 1
 
3.3%
132 1
 
3.3%
93 1
 
3.3%
59 1
 
3.3%
112 1
 
3.3%
57 1
 
3.3%
51 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
44 1
 
3.3%
51 1
 
3.3%
52 1
 
3.3%
57 1
 
3.3%
59 1
 
3.3%
63 1
 
3.3%
68 1
 
3.3%
69 3
10.0%
70 1
 
3.3%
71 1
 
3.3%
ValueCountFrequency (%)
134 1
3.3%
132 1
3.3%
121 1
3.3%
112 1
3.3%
104 1
3.3%
101 1
3.3%
98 1
3.3%
97 1
3.3%
95 1
3.3%
93 1
3.3%

RA
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean688.23333
Minimum525
Maximum844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:41.936485image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum525
5-th percentile595.45
Q1636.25
median695.5
Q3732.5
95-th percentile806.3
Maximum844
Range319
Interquartile range (IQR)96.25

Descriptive statistics

Standard deviation72.108005
Coefficient of variation (CV)0.10477261
Kurtosis-0.12256988
Mean688.23333
Median Absolute Deviation (MAD)55
Skewness0.045733975
Sum20647
Variance5199.5644
MonotonicityNot monotonic
2022-11-24T13:41:42.252656image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
641 1
 
3.3%
700 1
 
3.3%
731 1
 
3.3%
713 1
 
3.3%
627 1
 
3.3%
595 1
 
3.3%
809 1
 
3.3%
760 1
 
3.3%
678 1
 
3.3%
635 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
525 1
3.3%
595 1
3.3%
596 1
3.3%
608 1
3.3%
613 1
3.3%
618 1
3.3%
627 1
3.3%
635 1
3.3%
640 1
3.3%
641 1
3.3%
ValueCountFrequency (%)
844 1
3.3%
809 1
3.3%
803 1
3.3%
760 1
3.3%
754 1
3.3%
753 1
3.3%
737 1
3.3%
733 1
3.3%
731 1
3.3%
729 1
3.3%

ER
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean635.83333
Minimum478
Maximum799
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:42.551786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum478
5-th percentile538.3
Q1587.25
median644.5
Q3679.25
95-th percentile747.65
Maximum799
Range321
Interquartile range (IQR)92

Descriptive statistics

Standard deviation70.140786
Coefficient of variation (CV)0.11031316
Kurtosis0.17598784
Mean635.83333
Median Absolute Deviation (MAD)45.5
Skewness0.058709796
Sum19075
Variance4919.7299
MonotonicityNot monotonic
2022-11-24T13:41:42.895056image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
601 1
 
3.3%
653 1
 
3.3%
655 1
 
3.3%
659 1
 
3.3%
597 1
 
3.3%
553 1
 
3.3%
749 1
 
3.3%
698 1
 
3.3%
638 1
 
3.3%
577 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
478 1
3.3%
532 1
3.3%
546 1
3.3%
553 1
3.3%
557 1
3.3%
572 1
3.3%
577 1
3.3%
584 1
3.3%
597 1
3.3%
601 1
3.3%
ValueCountFrequency (%)
799 1
3.3%
749 1
3.3%
746 1
3.3%
700 1
3.3%
698 1
3.3%
694 1
3.3%
682 1
3.3%
680 1
3.3%
677 1
3.3%
664 1
3.3%

ERA
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9563333
Minimum2.94
Maximum5.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:43.195226image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum2.94
5-th percentile3.2775
Q13.6825
median4.025
Q34.22
95-th percentile4.6675
Maximum5.04
Range2.1
Interquartile range (IQR)0.5375

Descriptive statistics

Standard deviation0.45408858
Coefficient of variation (CV)0.11477511
Kurtosis0.34176563
Mean3.9563333
Median Absolute Deviation (MAD)0.29
Skewness0.053331339
Sum118.69
Variance0.20619644
MonotonicityNot monotonic
2022-11-24T13:41:43.497513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
3.73 1
 
3.3%
4.07 1
 
3.3%
4.09 1
 
3.3%
4.04 1
 
3.3%
3.72 1
 
3.3%
3.44 1
 
3.3%
4.69 1
 
3.3%
4.41 1
 
3.3%
4.02 1
 
3.3%
3.62 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
2.94 1
3.3%
3.21 1
3.3%
3.36 1
3.3%
3.43 1
3.3%
3.44 1
3.3%
3.57 1
3.3%
3.62 1
3.3%
3.67 1
3.3%
3.72 1
3.3%
3.73 1
3.3%
ValueCountFrequency (%)
5.04 1
3.3%
4.69 1
3.3%
4.64 1
3.3%
4.41 1
3.3%
4.33 1
3.3%
4.31 1
3.3%
4.28 1
3.3%
4.24 1
3.3%
4.16 1
3.3%
4.14 1
3.3%

CG
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4666667
Minimum0
Maximum11
Zeros3
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:43.737400image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35.75
95-th percentile7
Maximum11
Range11
Interquartile range (IQR)4.75

Descriptive statistics

Standard deviation2.7634727
Coefficient of variation (CV)0.7971556
Kurtosis0.077975489
Mean3.4666667
Median Absolute Deviation (MAD)2
Skewness0.73684527
Sum104
Variance7.6367816
MonotonicityNot monotonic
2022-11-24T13:41:43.927137image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 7
23.3%
2 4
13.3%
7 4
13.3%
3 3
10.0%
0 3
10.0%
5 3
10.0%
6 3
10.0%
4 2
 
6.7%
11 1
 
3.3%
ValueCountFrequency (%)
0 3
10.0%
1 7
23.3%
2 4
13.3%
3 3
10.0%
4 2
 
6.7%
5 3
10.0%
6 3
10.0%
7 4
13.3%
11 1
 
3.3%
ValueCountFrequency (%)
11 1
 
3.3%
7 4
13.3%
6 3
10.0%
5 3
10.0%
4 2
 
6.7%
3 3
10.0%
2 4
13.3%
1 7
23.3%
0 3
10.0%

SHO
Real number (ℝ)

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.3
Minimum4
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:44.163943image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4.9
Q19
median12
Q313
95-th percentile19.65
Maximum21
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1201774
Coefficient of variation (CV)0.36461747
Kurtosis0.73057643
Mean11.3
Median Absolute Deviation (MAD)2
Skewness0.56579043
Sum339
Variance16.975862
MonotonicityNot monotonic
2022-11-24T13:41:44.387885image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
12 7
23.3%
10 5
16.7%
13 3
10.0%
8 2
 
6.7%
9 2
 
6.7%
4 2
 
6.7%
15 2
 
6.7%
21 2
 
6.7%
7 2
 
6.7%
14 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
4 2
 
6.7%
6 1
 
3.3%
7 2
 
6.7%
8 2
 
6.7%
9 2
 
6.7%
10 5
16.7%
12 7
23.3%
13 3
10.0%
14 1
 
3.3%
15 2
 
6.7%
ValueCountFrequency (%)
21 2
 
6.7%
18 1
 
3.3%
15 2
 
6.7%
14 1
 
3.3%
13 3
10.0%
12 7
23.3%
10 5
16.7%
9 2
 
6.7%
8 2
 
6.7%
7 2
 
6.7%

SV
Real number (ℝ)

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.066667
Minimum28
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:44.624375image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile34.45
Q137.25
median42
Q346.75
95-th percentile58.2
Maximum62
Range34
Interquartile range (IQR)9.5

Descriptive statistics

Standard deviation7.8693352
Coefficient of variation (CV)0.1827245
Kurtosis0.39127553
Mean43.066667
Median Absolute Deviation (MAD)5
Skewness0.65752355
Sum1292
Variance61.926437
MonotonicityNot monotonic
2022-11-24T13:41:44.861738image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
35 4
 
13.3%
41 3
 
10.0%
45 3
 
10.0%
44 2
 
6.7%
48 2
 
6.7%
40 2
 
6.7%
56 1
 
3.3%
28 1
 
3.3%
47 1
 
3.3%
50 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
28 1
 
3.3%
34 1
 
3.3%
35 4
13.3%
36 1
 
3.3%
37 1
 
3.3%
38 1
 
3.3%
39 1
 
3.3%
40 2
6.7%
41 3
10.0%
43 1
 
3.3%
ValueCountFrequency (%)
62 1
 
3.3%
60 1
 
3.3%
56 1
 
3.3%
54 1
 
3.3%
50 1
 
3.3%
48 2
6.7%
47 1
 
3.3%
46 1
 
3.3%
45 3
10.0%
44 2
6.7%

E
Real number (ℝ)

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.333333
Minimum75
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size368.0 B
2022-11-24T13:41:45.145051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile77
Q186
median91
Q396.75
95-th percentile120.65
Maximum126
Range51
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation13.958889
Coefficient of variation (CV)0.14797409
Kurtosis0.014387954
Mean94.333333
Median Absolute Deviation (MAD)5
Skewness0.89013183
Sum2830
Variance194.85057
MonotonicityNot monotonic
2022-11-24T13:41:45.390361image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
88 3
 
10.0%
90 3
 
10.0%
86 3
 
10.0%
93 2
 
6.7%
77 2
 
6.7%
95 2
 
6.7%
122 1
 
3.3%
78 1
 
3.3%
75 1
 
3.3%
117 1
 
3.3%
Other values (11) 11
36.7%
ValueCountFrequency (%)
75 1
 
3.3%
77 2
6.7%
78 1
 
3.3%
79 1
 
3.3%
85 1
 
3.3%
86 3
10.0%
88 3
10.0%
90 3
10.0%
92 1
 
3.3%
93 2
6.7%
ValueCountFrequency (%)
126 1
3.3%
122 1
3.3%
119 1
3.3%
117 1
3.3%
116 1
3.3%
111 1
3.3%
101 1
3.3%
97 1
3.3%
96 1
3.3%
95 2
6.7%

Interactions

2022-11-24T13:41:30.837700image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:19.581519image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:23.973650image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:29.250950image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:33.941474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:38.491887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:43.380031image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:47.377365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:51.432733image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:55.551041image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:59.402484image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:04.885284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:10.268424image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:14.210666image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:18.473054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:22.550857image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:26.905251image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:31.061003image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:19.990089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:24.244759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:29.613982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:34.218018image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:38.803638image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:43.667513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:47.599314image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:51.668548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:55.791861image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:59.649459image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:05.127982image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:10.527603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:14.462207image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:18.692940image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:22.807881image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:27.162588image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:31.272889image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:20.220082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:24.485453image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:29.913467image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:34.563451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:39.081839image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:43.947402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:47.853405image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:51.907230image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:56.021942image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:59.894103image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:05.465548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:10.758013image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:14.689430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:18.918132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:23.042498image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:27.373239image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:31.485120image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:20.476391image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:24.745409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:30.146283image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:34.859254image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:39.384948image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:44.196123image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:48.069450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:52.129816image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:56.246447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:00.091172image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:05.776517image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:10.976808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:14.895189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:19.147802image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:23.307158image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:27.582423image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:31.773119image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:20.720080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:24.970398image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:30.453761image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:35.094680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:39.833372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:44.424383image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:48.295048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:52.394561image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:56.513676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:00.681916image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:06.168441image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:11.226583image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:15.147428image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:19.372759image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:23.574541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:27.861021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:32.109195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:20.990016image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:25.240387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:30.723629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:35.374222image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:40.146004image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:44.653164image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:48.509058image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:52.630487image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:56.737580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:00.915194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:06.450094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:11.451511image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:15.412068image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:19.584363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:23.863786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:28.092951image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:32.340691image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:21.273998image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:25.490627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:30.947696image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:40:40.505073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:40:48.734651image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:40:31.691092image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:40:41.012036image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:45.356339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:49.452867image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:53.326247image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:41:12.191509image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:16.116199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:20.354721image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:24.712264image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:28.798287image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:40:53.606005image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:57.636186image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:41:02.611008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:41:16.513578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:20.793986image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:25.172089image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:29.261710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:33.433440image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:22.460570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:26.872139image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:32.427796image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:40:41.892342image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:46.061363image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:50.136717image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:40:58.091169image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:02.985001image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:08.720567image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:12.862783image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:16.733354image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:21.042451image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:25.392349image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:29.479800image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:40:22.682655image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:27.285629image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:32.666414image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:40:42.133043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:40:50.354228image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:54.424298image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:58.307008image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:03.339927image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:08.995527image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:13.107961image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:17.347318image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:21.268082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:25.684049image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:29.699625image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:33.890676image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:22.906524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:27.705599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:32.947128image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:37.614864image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:42.348274image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:40:50.555463image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:41:29.918994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:34.079496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:23.115284image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:28.171901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:33.225430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:37.821933image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:42.659101image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:46.702883image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:50.753524image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:54.870406image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:58.751052image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:41:30.162965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:34.302419image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:23.485657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
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2022-11-24T13:41:18.065150image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:22.044731image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:26.429075image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:30.383896image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:34.505901image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:23.747194image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:28.935872image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:33.701772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:38.248434image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:43.122286image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:47.171381image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:51.229310image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:55.344630image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:40:59.178750image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:04.663828image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:10.019242image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:14.005307image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:18.273404image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:22.323438image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:26.694612image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-24T13:41:30.606076image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-24T13:41:45.658211image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-24T13:41:46.117257image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-24T13:41:46.532644image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-24T13:41:46.998530image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-24T13:41:47.409094image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-24T13:41:35.283267image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-24T13:41:35.818216image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

WRABH2B3BHRBBSOSBRAERERACGSHOSVE
09572455751497300421393839731046416013.73285688
18369654671349277441564391264707006534.072124586
28166954391395303291415331157866405843.6711103879
37662255331381260271364041231687016433.987937101
47468956051515289491514551259838037464.647123586
59389155091480308172325701151886706093.807103488
68776455671397272192125541227636986524.03344893
78171354851370246202174181331446936464.050104377
88064454851383278321674361310876426043.741126095
97874856401495294331614781148717536944.313104097
WRABH2B3BHRBBSOSBRAERERACGSHOSVE
209068355271351295171774881290516135573.431145088
218370354281363265131775391344576355773.624134190
2271613546314202364012037511501126786384.020123577
236757354201361251181004711107697606984.413104490
246362655291374272371303871274888097494.691735117
259266753851346263261875631258595955533.446214775
268469655651486288391364571159936275973.727184178
2779720564914942894815449013121327136594.041124486
287465054571324260361484261327827316554.09164192
296873755721479274491863881283978447995.04443695